DS-TransUNet:Dual Swin Transformer U-Net for Medical Image Segmentation

J. Silva, A. Histace, O. Romain, X. Dray, and B. Granado, “Toward embedded detection of polyps in wce images for early diagnosis of colorectal cancer,” International journal of computer assisted radiology and surgery , vol. 9, no. 2, pp. 283–293, 2014

2014

Earlier work this paper cites.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention . Springer, 2015, pp. 234–241

2015

Earlier work this paper cites.

J. Bernal, F. J. Sánchez, G. Fernández-Esparrach, D. Gil, C. Rodríguez, and F. Vilariño, “Wm-dova maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians,” Computerized Medical Imaging and Graphics , vol. 43, pp. 99–111, 2015

2015

Earlier work this paper cites.

N. Tajbakhsh, S. R. Gurudu, and J. Liang, “Automated polyp detection in colonoscopy videos using shape and context information,” IEEE transactions on medical imaging , vol. 35, no. 2, pp. 630–644, 2015

2015

Earlier work this paper cites.

J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 3431–3440

2015

Earlier work this paper cites.

Z. Cai, Q. Fan, R. S. Feris, and N. Vasconcelos, “A unified multi-scale deep convolutional neural network for fast object detection,” in European conference on computer vision . Springer, 2016, pp. 354–370

2016

Earlier work this paper cites.

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” arXiv preprint arXiv:1706.03762 , 2017

Original

2017

Earlier work this paper cites.

L.-C. Chen, G. Papandreou, F. Schroff, and H. Adam, “Rethinking atrous convolution for semantic image segmentation,” arXiv preprint arXiv:1706.05587 , 2017

Original

2017

Earlier work this paper cites.

S. Nah, T. Hyun Kim, and K. Mu Lee, “Deep multi-scale convolutional neural network for dynamic scene deblurring,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 3883–3891

2017

Earlier work this paper cites.

T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature pyramid networks for object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 2117–2125

2017

Earlier work this paper cites.

D. Vázquez, J. Bernal, F. J. Sánchez, G. Fernández-Esparrach, A. M. López, A. Romero, M. Drozdzal, and A. Courville, “A benchmark for endoluminal scene segmentation of colonoscopy images,” Journal of healthcare engineering , vol. 2017, 2017

2017

Earlier work this paper cites.

H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia, “Pyramid scene parsing network,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 2881–2890

2017

Earlier work this paper cites.

V. Badrinarayanan, A. Kendall, and R. Cipolla, “Segnet: A deep convolutional encoder-decoder architecture for image segmentation,” IEEE transactions on pattern analysis and machine intelligence , vol. 39, no. 12, pp. 2481–2495, 2017

2017

Earlier work this paper cites.

Z. Zhou, M. M. R. Siddiquee, N. Tajbakhsh, and J. Liang, “Unet++: A nested u-net architecture for medical image segmentation,” in Deep learning in medical image analysis and multimodal learning for clinical decision support . Springer, 2018, pp. 3–11

2018

Earlier work this paper cites.

X. Xiao, S. Lian, Z. Luo, and S. Li, “Weighted res-unet for high-quality retina vessel segmentation,” in 2018 9th international conference on information technology in medicine and education (ITME) . IEEE, 2018, pp. 327–331

2018

Earlier work this paper cites.

O. Oktay, J. Schlemper, L. L. Folgoc, M. Lee, M. Heinrich, K. Misawa, K. Mori, S. McDonagh, N. Y. Hammerla, B. Kainz et al. , “Attention u-net: Learning where to look for the pancreas,” arXiv preprint arXiv:1804.03999 , 2018

Original

2018

Earlier work this paper cites.

X. Li, H. Chen, X. Qi, Q. Dou, C.-W. Fu, and P.-A. Heng, “H-denseunet: hybrid densely connected unet for liver and tumor segmentation from ct volumes,” IEEE transactions on medical imaging , vol. 37, no. 12, pp. 2663–2674, 2018

2018

Earlier work this paper cites.

M. Z. Alom, M. Hasan, C. Yakopcic, T. M. Taha, and V. K. Asari, “Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation,” arXiv preprint arXiv:1802.06955 , 2018

Original

2018

Earlier work this paper cites.

X. Wang, R. Girshick, A. Gupta, and K. He, “Non-local neural networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 7794–7803

2018

Cited alongside, same era.

C.-F. Chen, Q. Fan, N. Mallinar, T. Sercu, and R. Feris, “Big-little net: An efficient multi-scale feature representation for visual and speech recognition,” arXiv preprint arXiv:1807.03848 , 2018

Original

2018

Cited alongside, same era.

P. Tschandl, C. Rosendahl, and H. Kittler, “The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions,” Scientific data , vol. 5, no. 1, pp. 1–9, 2018

2018

Cited alongside, same era.

L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, “Encoder-decoder with atrous separable convolution for semantic image segmentation,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 801–818

2018

Cited alongside, same era.

D. Jha, M. A. Riegler, D. Johansen, P. Halvorsen, and H. D. Johansen, “Doubleu-net: A deep convolutional neural network for medical image segmentation,” in 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS) . IEEE, 2020, pp. 558–564

2020

Later among the works it cites.

H. Touvron, M. Cord, M. Douze, F. Massa, A. Sablayrolles, and H. Jégou, “Training data-efficient image transformers & distillation through attention,” arXiv preprint arXiv:2012.12877 , 2020

Original

2020

Later among the works it cites.

B. Cheng, B. Xiao, J. Wang, H. Shi, T. S. Huang, and L. Zhang, “Higherhrnet: Scale-aware representation learning for bottom-up human pose estimation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 5386–5395

2020

Later among the works it cites.

D. Jha, P. H. Smedsrud, M. A. Riegler, P. Halvorsen, T. de Lange, D. Johansen, and H. D. Johansen, “Kvasir-seg: A segmented polyp dataset,” in International Conference on Multimedia Modeling . Springer, 2020, pp. 451–462